Stifanelli, P. F. and Creanza, T. M. and Anglani, R. and Liuzzi, V. C. and Mukherjee, S. and Ancona, N. (2012) A comparative study of Gaussian Graphical Model approaches for genomic data. Il nuovo cimento C, 35 (Sup. 1). pp. 119-127. ISSN 1826-9885
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Abstract
The inference of networks of dependencies by Gaussian Graphical Models on high-throughput data is an open issue in modern molecular biology. In this paper we provide a comparative study of three methods to obtain small sample and high dimension estimates of partial correlation coefficients: the Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and covarianceregularized method (2C ). We first compare them on simulated datasets and we find that PINV is less stable in terms of AUC performance when the number of variables changes. The two regularized methods have comparable performances but 2C is much faster than RCM. Finally, we present the results of an application of 2C for the inference of a gene network for isoprenoid biosynthesis pathways in Arabidopsis thaliana.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Stochastic modeling ; Regulatory genetic and chemical networks ; Systems biology |
| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 27 Apr 2020 15:53 |
| Last Modified: | 27 Apr 2020 15:53 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/18043 |
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